Advertisement

Patient-Specific Cranial Nerve Identification Using a Discrete Deformable Contour Model for Skull Base Neurosurgery Planning and Simulation

  • Sharmin Sultana
  • Jason E. Blatt
  • Yueh Lee
  • Matthew Ewend
  • Justin S Cetas
  • Anthony Costa
  • Michel A. Audette
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9401)

Abstract

In this paper, we present a minimally supervised method for the identification of the intra-cranial portion of cranial nerves, using a novel, discrete 1-Simplex 3D active contour model. The clinical applications include planning and personalized simulation of skull base neurosurgery. The centerline of a cranial nerve is initialized from two user-supplied end points by computing a Minimal Path. The 1-Simplex is a Newtonian model for vertex motion, where every non-endpoint vertex has 2-connectivity with neighboring vertices, with which it is linked by edges. The segmentation behavior of the model is governed by the equilibrium between internal and external forces. The external forces include an image force that favors a centered path within high-vesselness points. The method is validated quantitatively using synthetic and real MRI datasets.

Keywords

Cranial nerves Centerline Simplex Minimal path Vesselness Neurosurgery planning, personalized neurosurgery simulation 

Notes

Acknowledgements

We would like to thank John Butman, M.D., of NIH for contributing MRI data.

References

  1. 1.
    Antoniadis, G., et al.: Iatrogenic nerve injuries: Prevalence, diagnosis and treatment. Deutsches Ärzteblatt Int. 111(16), 273 (2014)Google Scholar
  2. 2.
    Nowinski, W.L., et al.: Three-dimensional interactive and stereotactic atlas of the cranial nerves and their nuclei correlated with surface neuroanatomy, vasculature and magnetic resonance imaging. J. Neurosci. Methods 206(2), 205–216 (2012)CrossRefGoogle Scholar
  3. 3.
    Lesage, D., et al.: A review of 3D vessel lumen segmentation techniques: Models, features and extraction schemes. Med. Img. Anal. 13(6), 819–845 (2009)CrossRefGoogle Scholar
  4. 4.
    Noble, J.H., Dawant, B.M.: An atlas-navigated optimal medial axis and deformable model algorithm (NOMAD) for the segmentation of the optic nerves and chiasm in MR and CT images. Med. Img. Anal. 15(6), 877–884 (2011)CrossRefGoogle Scholar
  5. 5.
    Delingette, H.: General object reconstruction based on simplex meshes. Int. J. Comput. Vis. 32(2), 111–146 (1999)CrossRefGoogle Scholar
  6. 6.
    Gilles, B., et al.: Musculoskeletal MRI segmentation using multi-resolution simplex meshes with medial representations. Med. Img. Anal. 14(3), 291–302 (2010)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Tejos, C.: Simplex mesh diffusion snakes: integrating 2D and 3D deformable models and statistical shape knowledge in a variational framework. Int. J. Comp. Vis. 85, 19–34 (2009)CrossRefGoogle Scholar
  8. 8.
    Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A.: Multiscale vessel enhancement filtering. In: Wells, W.M., Colchester, A.C., Delp, S.L. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 130–137. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  9. 9.
    Deschamps, T., Cohen, L.D.: Fast extraction of tubular and tree 3D surfaces with front propagation methods. IEEE Trans. Patt. Rec. Mach. Intel. (2002) Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Sharmin Sultana
    • 1
  • Jason E. Blatt
    • 2
  • Yueh Lee
    • 2
  • Matthew Ewend
    • 2
  • Justin S Cetas
    • 3
  • Anthony Costa
    • 4
  • Michel A. Audette
    • 1
  1. 1.Deptartment of MSVEOld Dominion UniversityNorfolkUSA
  2. 2.Department of Neurosurgery and RadiologyUniversity of North CarolinaChapel HillUSA
  3. 3.Department of NeurosurgeryOregon Health and Science UniversityPortlandUSA
  4. 4.Department of NeurosurgeryIcahn School of Medicine at Mount SinaiNew YorkUSA

Personalised recommendations